A1 Vertaisarvioitu alkuperäisartikkeli tieteellisessä lehdessä
Area-Based Approach for Mapping and Monitoring Riverine Vegetation Using Mobile Laser Scanning
Tekijät: Saarinen N, Vastaranta M, Vaaja M, Lotsari E, Jaakkola A, Kukko A, Kaartinen H, Holopainen M, Hyyppä H, Alho P
Kustantaja: MDPI AG
Julkaisuvuosi: 2013
Journal: Remote Sensing
Tietokannassa oleva lehden nimi: REMOTE SENSING
Lehden akronyymi: REMOTE SENS-BASEL
Numero sarjassa: 10
Vuosikerta: 5
Numero: 10
Aloitussivu: 5285
Lopetussivu: 5303
Sivujen määrä: 19
ISSN: 2072-4292
DOI: https://doi.org/10.3390/rs5105285
Tiivistelmä
Vegetation plays an important role in stabilizing the soil and decreasing fluvial erosion. In certain cases, vegetation increases the accumulation of fine sediments. Efficient and accurate methods are required for mapping and monitoring changes in the fluvial environment. Here, we develop an area-based approach for mapping and monitoring the vegetation structure along a river channel. First, a 2 x 2 m grid was placed over the study area. Metrics describing vegetation density and height were derived from mobile laser-scanning (MLS) data and used to predict the variables in the nearest-neighbor (NN) estimations. The training data were obtained from aerial images. The vegetation cover type was classified into the following four classes: bare ground, field layer, shrub layer, and canopy layer. Multi-temporal MLS data sets were applied to the change detection of riverine vegetation. This approach successfully classified vegetation cover with an overall classification accuracy of 72.6%; classification accuracies for bare ground, field layer, shrub layer, and canopy layer were 79.5%, 35.0%, 45.2% and 100.0%, respectively. Vegetation changes were detected primarily in outer river bends. These results proved that our approach was suitable for mapping riverine vegetation.
Vegetation plays an important role in stabilizing the soil and decreasing fluvial erosion. In certain cases, vegetation increases the accumulation of fine sediments. Efficient and accurate methods are required for mapping and monitoring changes in the fluvial environment. Here, we develop an area-based approach for mapping and monitoring the vegetation structure along a river channel. First, a 2 x 2 m grid was placed over the study area. Metrics describing vegetation density and height were derived from mobile laser-scanning (MLS) data and used to predict the variables in the nearest-neighbor (NN) estimations. The training data were obtained from aerial images. The vegetation cover type was classified into the following four classes: bare ground, field layer, shrub layer, and canopy layer. Multi-temporal MLS data sets were applied to the change detection of riverine vegetation. This approach successfully classified vegetation cover with an overall classification accuracy of 72.6%; classification accuracies for bare ground, field layer, shrub layer, and canopy layer were 79.5%, 35.0%, 45.2% and 100.0%, respectively. Vegetation changes were detected primarily in outer river bends. These results proved that our approach was suitable for mapping riverine vegetation.
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